Simulating Bosonic Fractional Quantum Hall States using Deep Learning
POSTER
Abstract
We explore a machine learning-inspired variational framework for investigating strongly correlated phases in bosonic fractional quantum Hall systems. By leveraging a self-attention-based neural quantum state architecture, we aim to capture the complex entanglement structure and non-local correlations inherent to topologically ordered phases. Our approach opens a promising pathway toward scalable modelling of nontrivial quantum many-body phenomena.
*This work was supported by the EPSRC [grant number EP/V062654/1], a Simons Investigator Award [Grant No. 511029] and a Cambridge International Scholarship provided by the Cambridge Trust.
Presenters
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Daniel Spasic-Mlacak
- University of Cambridge